### 代码实现思路

#### python

import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import numpy as np
import random
import spacy

# GloVe随机模拟文本特征（假设每个文本的词向量维度为300）

class GloVeEmbedding(nn.Module):
    def __init__(self, embedding_dim, vocab_size):
        super(GloVeEmbedding, self).__init__()
        self.embedding = nn.Embedding(vocab_size, embedding_dim)

    def forward(self, x):
        return self.embedding(x)

# GCN层

class GCNLayer(nn.Module):
    def __init__(self, input_dim, output_dim):
        super(GCNLayer, self).__init__()
        self.weight = nn.Parameter(torch.FloatTensor(input_dim, output_dim))
        self.reset_parameters()

    def reset_parameters(self):
        nn.init.xavier_uniform_(self.weight)

    def forward(self, x, adj):
        h = torch.matmul(x, self.weight)
        output = torch.matmul(adj, h)
        return F.relu(output)

# 自注意力层
class SelfAttention(nn.Module):
    def __init__(self, embed_dim, heads):
        super(SelfAttention, self).__init__()
        self.embed_dim = embed_dim
        self.heads = heads
        self.values = nn.Linear(embed_dim, embed_dim, bias=False)
        self.keys = nn.Linear(embed_dim, embed_dim, bias=False)
        self.queries = nn.Linear(embed_dim, embed_dim, bias=False)
        self.fc_out = nn.Linear(embed_dim, embed_dim)

    def forward(self, x):
        batch_size, seq_len, embed_dim = x.size()
        values = self.values(x)
        keys = self.keys(x)
        queries = self.queries(x)
        
        # 多头自注意力机制
        energy = torch.bmm(queries, keys.transpose(1, 2))
        attention = torch.softmax(energy / (embed_dim ** (1/2)), dim=2)
        
        out = torch.bmm(attention, values)
        out = self.fc_out(out)
        return out

# 整体模型
class SentimentGCNAttentionModel(nn.Module):
    def __init__(self, vocab_size, embedding_dim, gcn_dim, hidden_dim, num_classes):
        super(SentimentGCNAttentionModel, self).__init__()
        self.embedding = GloVeEmbedding(embedding_dim, vocab_size)
        self.gcn1 = GCNLayer(embedding_dim, gcn_dim)
        self.gcn2 = GCNLayer(gcn_dim, gcn_dim)
        self.lstm = nn.LSTM(gcn_dim, hidden_dim, batch_first=True)
        self.attention = SelfAttention(hidden_dim, heads=8)
        self.fc = nn.Linear(hidden_dim, num_classes)

    def forward(self, x, adj):
        embed = self.embedding(x)
        gcn_out = self.gcn1(embed, adj)
        gcn_out = self.gcn2(gcn_out, adj)
        lstm_out, _ = self.lstm(gcn_out)
        attention_out = self.attention(lstm_out)
        logits = self.fc(attention_out[:, -1, :])  # 取最后时间步的输出进行分类
        return logits

# 数据准备

batch_size = 32
seq_len = 10
vocab_size = 5000
embedding_dim = 300
gcn_dim = 128
hidden_dim = 256
num_classes = 3

# 模拟输入

x = torch.randint(0, vocab_size, (batch_size, seq_len))  # 随机文本输入
adj = torch.rand(batch_size, seq_len, seq_len)  # 随机邻接矩阵

# 创建模型

model = SentimentGCNAttentionModel(vocab_size, embedding_dim, gcn_dim, hidden_dim, num_classes)

# 前向传播

output = model(x, adj)
print(output.shape)  # 输出维度: [batch_size, num_classes]